The Search for Objective Insight
The recent worldwide market correction and increased volatility has emphasized the need investors have for guidance. The perennial questions of when to buy or sell and what proportion an asset should hold in a portfolio are the topics of countless articles, opinions and theories. Investment advisors and analysts offer these services for sizeable fees. But in the final analysis most investment models are privately held processes, with little or no transparency. Or they are simply intuitive suggestions based on personal experience. From common financial rules of thumb to actively managed and exotic private funds there is a notable lack of critical comparative data, theoretical foundation or statistical evidence if an investment methodology is effective or when it is effective. There are numerous trading platforms that have emerged in recent decades that are easily accessible to the average investor with web access. However, these tools tend to focus on active trading of single assets. What the investor needs is an easily used toolset that is transparent, has demonstrable effectiveness and focuses on a methodology that targets modest trading activity, reduced volatility, smaller draw-downs and enhanced returns across market corrections.
The Near Random Nature of the Market
There is well documented empirical evidence that the broad market deviates from a purely random pricing variable. Jeremy Siegel, in his book, “Stocks for the Long Run”, presents well known data that indicates long term market performance deviates from a purely random variable as evidenced by the shrinking volatility (square root relationship of the standard deviation is violated) of bonds and stocks as the time frame expands over years and decades. This is attributed to the mean regressing nature of the market. There are other long term statistical models such as Eliot wave theories that ascribe market pricing behavior to multiple interacting cycles of fundamental market drivers. And there are shorter term trading models such as the 200 day moving average methodology that can take advantage of shorter term trends if one can tolerate the trading volume, false positive over and back trades and slippage constraints. There is scant theoretical foundation for these trading approaches and they are mostly predicated on the empirical tests that seem to indicate they have been successful in the past, given the constraints mentioned above.
The Market as a Gamble
The apparent randomness of the market appears in many ways to make it a gamble. However, there are fundamental and statistical relationships that may tilt the odds slightly in favor of the investor, not unlike counting cards. But any method must uncover and monitor those relationships in real time if they are to be used to advantage.
Mean Reversion
Empirical and historical evidence implies that the market is mean reverting. Price to earnings ratios (PE) have been a fundamental tool of the financial analyst to predict future returns. And over a long enough time horizon of perhaps 5 or 10 years or longer, this apparent relationship can benefit the investor greatly. However, over shorter time horizons of weeks, months or a few years this is a poor predictor of returns where near term market volatility can hide the longer term relationships. This can be seen in FIG. 1 which shows PE as a leading indicator of further returns and in FIG. 2, a scatter plot of PE as a predictor of future returns.
Referring to FIG. 2, another way to illustrate this relationship, with a clearer view into its predictive nature, is to look at a scatter plot of the future returns of a market with respect to an underling metric, in this case PE, and with a linear fit the trend superimposed on the noise becomes apparent.
A technical surrogate for the more fundamental metric of PE ratio, is the mean deviation of the price as illustrated in FIG. 3 (mean deviation as a leading indicator of future returns) and FIG. 4 (scatter plot of mean deviation as a predictor of future returns).
Modern Portfolio Theory
Modern Portfolio Theory (MPT) has supporters and critics. In brief, it is a theory predicated on diversification as a means of reducing volatility. To that end it is a reasonable and fundamental approach to building portfolios that reduce market fluctuations by constructing them of multiple uncorrelated (or better, negatively correlated) assets. Invariably those relationships are viewed as historical and static and are not particularly forward looking. The assumption is that historical performance will reflect future performance and that the relationships of the past will continue into the future. Furthermore, in an effort to quench volatility one must typically sacrifice return. In the classical view, by maximizing the Sharpe ratio one can maximize risk adjusted returns. But that is a backward looking view and does not necessarily represent an optimal forward looking strategy.
Dynamic Asset allocation
An extension to MPT is the concept of dynamic or active asset allocation that weights the uncorrelated or negatively correlated asset classes based on historical norms of the market as it moves through different phases and rotates from one favored asset class to another. This is especially relevant as major market dislocations occur which become guide posts for those sector rotations that have been observed in the past.
Efficient Market Theory
John Keynes, the influential British economist, once said, “The market can stay irrational longer than you can stay solvent,” and it is an observation that is equally true today. This observation refers to the fact that the markets are not always rational and therefore there are times when the market undervalues companies and overvalues companies. This goes to the heart of the Efficient Market Theory (EMT) which assumes that prices in the market efficiently represent the true underlying values. MPT assumes an efficient market. But Keynes' observation would imply that markets are not efficient. In the short run, markets appear very inefficient, leading to bubbles and crashes. In the long run as markets and sectors correct for irrational behavior and revert to their mean they appear far more efficient. Fundamental analysis is a powerful analytical tool, but it may not optimally deal with inefficient markets.